In this analysis, we will directly test whether an ROI represents positive and negative wins in a similar manner. This will be done by correlating the multi-voxel representation of positive win trials to negative win trials. The hypothesis here is that the posterior cerebellum will respond similarily when participants were correct about someone liking them as being correct about someone disliking them, and that this similarity will be greater than in the ventral striatum.
Divide data by task
Filter data by hypothesized ROIs
Fischer r to z transform (this is not used)
These t-tests will examine whether each ROI had significantly greater similarity than 0
## Df Sum Sq Mean Sq F value Pr(>F)
## roi 2 0.0640 0.03202 6.168 0.00256 **
## Residuals 180 0.9344 0.00519
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## region02 region08 striatum_ventral
## mean 0.0612427 0.06177925 0.02183083
## sd 0.0635708 0.09355805 0.05270933
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = r ~ roi, data = all_sim_vcr_mdoors_rois)
##
## $roi
## diff lwr upr p adj
## region08-region02 0.0005365529 -0.03029410 0.031367202 0.9990678
## striatum_ventral-region02 -0.0394118682 -0.07024252 -0.008581219 0.0080823
## striatum_ventral-region08 -0.0399484212 -0.07077907 -0.009117772 0.0071204
Specify ROI plotting order on the x-axis
In this analysis we will create theorical RDMs, that predict a specific representation within an ROI.
Being correct about someone not liking you is different from being
correct about someone liking you. But being incorrect about someone not
liking you is the same feeling as being incorrect about someone not
liking out (negative).
Being correct about someone not liking you is different from being
correct about someone liking you. But being incorrect about someone not
liking you is the same feeling as being incorrect about someone not
liking out (negative).
Filter data by hypothesized ROIs
## Df Sum Sq Mean Sq F value Pr(>F)
## roi 2 0.223 0.11173 1.347 0.263
## Residuals 180 14.926 0.08292
## region02 region08 striatum_ventral
## mean 0.2145690 0.2434223 0.1592058
## sd 0.2955092 0.2826002 0.2856290
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = r ~ roi, data = data_tdomain_rois)
##
## $roi
## diff lwr upr p adj
## region08-region02 0.02885330 -0.09437285 0.15207945 0.8448882
## striatum_ventral-region02 -0.05536319 -0.17858934 0.06786296 0.5390475
## striatum_ventral-region08 -0.08421649 -0.20744264 0.03900966 0.2418751
## Df Sum Sq Mean Sq F value Pr(>F)
## roi 2 0.05 0.02490 0.399 0.672
## Residuals 180 11.24 0.06245
## region02 region08 striatum_ventral
## mean -0.08846393 -0.1034032 -0.06341868
## sd 0.24809676 0.2577733 0.24364368
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = r ~ roi, data = data_valence_rois)
##
## $roi
## diff lwr upr p adj
## region08-region02 -0.01493927 -0.12187963 0.09200109 0.9417107
## striatum_ventral-region02 0.02504525 -0.08189511 0.13198561 0.8448285
## striatum_ventral-region08 0.03998452 -0.06695584 0.14692489 0.6513341
## Df Sum Sq Mean Sq F value Pr(>F)
## roi 2 0.0079 0.003935 0.437 0.647
## Residuals 180 1.6207 0.009004
## region02 region08 striatum_ventral
## mean -0.008641344 -0.007030246 0.006005002
## sd 0.089935872 0.094721741 0.099751198
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = r ~ roi, data = data_outcome_rois)
##
## $roi
## diff lwr upr p adj
## region08-region02 0.001611098 -0.03899318 0.04221538 0.9951643
## striatum_ventral-region02 0.014646346 -0.02595793 0.05525063 0.6708993
## striatum_ventral-region08 0.013035248 -0.02756903 0.05363953 0.7287671
## Df Sum Sq Mean Sq F value Pr(>F)
## roi 2 0.107 0.05339 2.504 0.0846 .
## Residuals 180 3.837 0.02132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## region02 region08 striatum_ventral
## mean -0.01766416 -0.03709473 0.02102035
## sd 0.15169658 0.13782806 0.14815226
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = r ~ roi, data = data_outcval_rois)
##
## $roi
## diff lwr upr p adj
## region08-region02 -0.01943057 -0.081911508 0.04305036 0.7430735
## striatum_ventral-region02 0.03868451 -0.023796428 0.10116544 0.3111227
## striatum_ventral-region08 0.05811508 -0.004365854 0.12059601 0.0742090
## Df Sum Sq Mean Sq F value Pr(>F)
## roi 2 0.011 0.00539 0.136 0.873
## Residuals 180 7.141 0.03967
## region02 region08 striatum_ventral
## mean 0.08384646 0.0894832 0.07112466
## sd 0.20692467 0.1841102 0.20568750
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = r ~ roi, data = data_intract_rois)
##
## $roi
## diff lwr upr p adj
## region08-region02 0.005636737 -0.07959765 0.09087113 0.9866253
## striatum_ventral-region02 -0.012721802 -0.09795619 0.07251259 0.9337446
## striatum_ventral-region08 -0.018358539 -0.10359293 0.06687585 0.8670500
Fix up dataframe for pretty table
| ROI | Mean | df | t-statistic | p | CI | |
|---|---|---|---|---|---|---|
| 14 | CB Region 2 | 0.08 | 60 | 3.16 | 0.004 | [ 0.03 , 0.14 ] |
| 13 | CB Region 8 | 0.09 | 60 | 3.80 | 0.001 | [ 0.04 , 0.14 ] |
| 15 | Ventral Striatum | 0.07 | 60 | 2.70 | 0.009 | [ 0.02 , 0.12 ] |
| 8 | CB Region 2 | -0.01 | 60 | -0.75 | 0.640 | [ -0.03 , 0.01 ] |
| 7 | CB Region 8 | -0.01 | 60 | -0.58 | 0.640 | [ -0.03 , 0.02 ] |
| 9 | Ventral Striatum | 0.01 | 60 | 0.47 | 0.640 | [ -0.02 , 0.03 ] |
| 11 | CB Region 2 | -0.02 | 60 | -0.91 | 0.367 | [ -0.06 , 0.02 ] |
| 10 | CB Region 8 | -0.04 | 60 | -2.10 | 0.119 | [ -0.07 , 0 ] |
| 12 | Ventral Striatum | 0.02 | 60 | 1.11 | 0.367 | [ -0.02 , 0.06 ] |
| 2 | CB Region 2 | 0.21 | 60 | 5.67 | 0.000 | [ 0.14 , 0.29 ] |
| 1 | CB Region 8 | 0.24 | 60 | 6.73 | 0.000 | [ 0.17 , 0.32 ] |
| 3 | Ventral Striatum | 0.16 | 60 | 4.35 | 0.000 | [ 0.09 , 0.23 ] |
| 5 | CB Region 2 | -0.09 | 60 | -2.78 | 0.011 | [ -0.15 , -0.02 ] |
| 4 | CB Region 8 | -0.10 | 60 | -3.13 | 0.008 | [ -0.17 , -0.04 ] |
| 6 | Ventral Striatum | -0.06 | 60 | -2.03 | 0.046 | [ -0.13 , 0 ] |
| 17 | CB Region 2 | 0.06 | 60 | 7.52 | 0.000 | [ 0.04 , 0.08 ] |
| 16 | CB Region 8 | 0.06 | 60 | 5.16 | 0.000 | [ 0.04 , 0.09 ] |
| 18 | Ventral Striatum | 0.02 | 60 | 3.23 | 0.002 | [ 0.01 , 0.04 ] |
| 20 | CB Region 2 | 0.07 | 60 | 9.00 | 0.000 | [ 0.06 , 0.09 ] |
| 19 | CB Region 8 | 0.12 | 60 | 6.96 | 0.000 | [ 0.08 , 0.15 ] |
| 21 | Ventral Striatum | 0.02 | 60 | 3.65 | 0.001 | [ 0.01 , 0.03 ] |
Research Question: Does greater similarity for positive and negative wins relate to anxiety and depression?
Hypothesis 1: Greater similarity between positive and negative wins in the ventral striatum will be related to greater anxiety and less depression (Quarmley replication)
Hypothesis 2: Greater similarity between positive and negative wins in the cerebellum …
## Df Sum Sq Mean Sq F value Pr(>F)
## ch_totanx 1 0.0148 0.01479 0.570 0.458
## dep_group 1 0.0101 0.01012 0.390 0.539
## ch_totanx:dep_group 1 0.0044 0.00444 0.171 0.683
## Residuals 22 0.5708 0.02594
## Df Sum Sq Mean Sq F value Pr(>F)
## ch_totanx 1 0.0148 0.01479 0.606 0.444
## Residuals 24 0.5853 0.02439
## [1] -0.04553422
## Df Sum Sq Mean Sq F value Pr(>F)
## ch_totanx 1 0.0148 0.014789 0.559 0.463
## ch_cdi_total 1 0.0009 0.000893 0.034 0.856
## ch_totanx:ch_cdi_total 1 0.0024 0.002442 0.092 0.764
## Residuals 22 0.5820 0.026454
## Df Sum Sq Mean Sq F value Pr(>F)
## ch_totanx 1 0.0148 0.014789 0.559 0.463
## ch_cdi_total 1 0.0009 0.000893 0.034 0.856
## ch_totanx:ch_cdi_total 1 0.0024 0.002442 0.092 0.764
## Residuals 22 0.5820 0.026454
##
## Call:
## lm(formula = z ~ age, data = vs_vcr_social_fltr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.095566 -0.028568 0.001382 0.028446 0.096639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.040064 0.022327 1.794 0.0779 .
## age -0.001107 0.001261 -0.878 0.3836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.04535 on 59 degrees of freedom
## Multiple R-squared: 0.01289, Adjusted R-squared: -0.003839
## F-statistic: 0.7705 on 1 and 59 DF, p-value: 0.3836
##
## Call:
## lm(formula = z ~ age, data = cb_vcr_social_fltr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.25992 -0.09287 -0.02045 0.09230 0.56124
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.065598 0.069411 0.945 0.348
## age 0.003257 0.003920 0.831 0.409
##
## Residual standard error: 0.141 on 59 degrees of freedom
## Multiple R-squared: 0.01157, Adjusted R-squared: -0.005185
## F-statistic: 0.6905 on 1 and 59 DF, p-value: 0.4093
2.4 Social
2.4.1 ANOVA
2.4.2 Post-hoc Tests
2.4.3 Visualization